2024-10-09-verma24a.md

title	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
ASML: A Scalable and Efficient AutoML Solution for Data Streams	9fL1HaSW6X	Online learning poses a significant challenge to AutoML, as the best model and configuration may change depending on the data distribution. To address this challenge, we propose Automated Streaming Machine Learning (ASML), an online learning framework that automatically finds the best machine learning models and their configurations for changing data streams. It adapts to the online learning scenario by continuously exploring a large and diverse pipeline configuration space. It uses an adaptive optimisation technique that utilizes the current best design, adaptive random directed nearby search, and an ensemble of best performing pipelines. We experimented with real and synthetic drifting data streams and showed that ASML can build accurate and adaptive pipelines by constantly exploring and responding to changes. In several datasets, it outperforms existing online AutoML and state-of-the-art online learning algorithms.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	verma24a	0	ASML: A Scalable and Efficient AutoML Solution for Data Streams	11/1	26	11/1-26	11	false	Verma, Nilesh and Bifet, Albert and Pfahringer, Bernhard and Bahri, Maroua	given family Nilesh Verma given family Albert Bifet given family Bernhard Pfahringer given family Maroua Bahri	2024-10-09		Proceedings of the Third International Conference on Automated Machine Learning	256	inproceedings	date-parts 2024 10 9	https://raw.githubusercontent.com/mlresearch/v256/main/assets/verma24a/verma24a.pdf